
> ### Preliminaries
> source("psrm_scriptX.R")

The determinants of revenue shares in total taxation, 1870-1913
-----------------------------------------------------------------------------------
                              Domestic Indirect Share          Direct Share        
                             Model 1  Model 2  Model 3  Model 4   Model 5  Model 6 
-----------------------------------------------------------------------------------
Labour trade advantage (LTA)  -0.16  -0.76***  -0.52**   -0.04    0.79***  0.49*** 
                             (0.17)   (0.17)    (0.21)   (0.13)   (0.12)    (0.14) 
Inequality                    -0.10  -0.57***  -0.50**  -1.10*** -0.34***  -0.48***
                             (0.14)   (0.16)    (0.20)   (0.11)   (0.11)    (0.13) 
LTA : inequality             0.53**   1.58***  1.29***   -0.04   -1.51***  -1.09***
                             (0.25)   (0.27)    (0.33)   (0.19)   (0.19)    (0.22) 
GDP per capita                        0.0000    0.0000           0.0000***  0.0000 
                                     (0.0000)  (0.0000)          (0.0000)  (0.0000)
Vote-tax link                        -0.07***  -0.07***          -0.05***  -0.04***
                                      (0.02)    (0.02)            (0.01)    (0.01) 
Economic franchise                   -0.04***   -0.01             -0.003     0.01  
                                      (0.02)    (0.04)            (0.01)    (0.03) 
Trade                                -27.96***  10.37            25.53***   -2.02  
                                      (4.92)   (14.12)            (3.43)    (9.35) 
Tax/GDP                                        -0.01**                      0.01*  
                                               (0.004)                     (0.003) 
Country fixed effects           Y        Y        Y        Y         Y        Y    
Year fixed effects              Y        Y        Y        Y         Y        Y    
N                              425      380      306      425       380      306   
R-squared                     0.94     0.96      0.96     0.95     0.97      0.97  
Adj. R-squared                0.94     0.95      0.95     0.94     0.96      0.96  
-----------------------------------------------------------------------------------
***p < .01; **p < .05; *p < .1                                                     

The determinants of top direct tax rates, 1870-1913
----------------------------------------------------------------------------------------
                                  Top Income Tax Rate        Top Inheritance Tax Rate   
                              Model 1   Model 2   Model 3   Model 4   Model 5   Model 6 
----------------------------------------------------------------------------------------
Labour trade advantage (LTA)   1.86      -3.04   -11.72**  25.13***  15.76***    8.94   
                              (4.83)    (4.95)    (5.89)    (4.16)    (4.69)    (5.93)  
Inequality                   -20.66*** -32.79*** -33.85***   -1.42   -12.65*** -17.22***
                              (4.16)    (4.66)    (5.56)    (3.58)    (4.42)    (5.60)  
LTA : inequality               0.52      8.15     22.00**  -45.63*** -30.02***  -18.32* 
                              (7.32)    (7.74)    (9.27)    (6.30)    (7.34)    (9.33)  
GDP per capita                         -0.001*** -0.001***           -0.001*** -0.002***
                                       (0.0003)  (0.0004)            (0.0003)  (0.0004) 
Vote-tax link                          -3.99***  -2.78***              -0.51     0.16   
                                        (0.48)    (0.56)              (0.45)    (0.56)  
Economic franchise                       -0.30    3.47***              -0.53     1.30   
                                        (0.44)    (1.23)              (0.41)    (1.24)  
Trade                                  308.99**   -518.67            -264.91** -731.99* 
                                       (134.47)  (397.43)            (127.51)  (400.24) 
Tax/GDP                                            0.11                          -0.18  
                                                  (0.12)                        (0.12)  
Country fixed effects            Y         Y         Y         Y         Y         Y    
Year fixed effects               Y         Y         Y         Y         Y         Y    
N                               397       386       306       397       386       306   
R-squared                      0.90      0.92      0.93      0.88      0.88      0.89   
Adj. R-squared                 0.88      0.90      0.91      0.85      0.86      0.87   
----------------------------------------------------------------------------------------
***p < .01; **p < .05; *p < .1                                                          

Ease of labor election in 1906. Model 4 provides estimates for figure~5
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
                                                                                                 Dependent variable: ordered factor.                                                  
                                                         Model 1                    Model 2                    Model 3                    Model 4                    Model 5          
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Free trade                                               1.49***                    1.86***                    1.98***                    2.32***                    2.29***          
                                                         (0.18)                      (0.17)                     (0.16)                     (0.16)                     (0.19)          
Neutral trade                                            0.57***                    0.59***                    0.77***                    0.61***                    1.19***          
                                                         (0.21)                      (0.19)                     (0.17)                     (0.15)                     (0.16)          
Double member                                                                                                                             2.77***                    2.45***          
                                                                                                                                           (0.35)                     (0.38)          
County                                                                                                                                    -1.88***                   -1.35***         
                                                                                                                                           (0.37)                     (0.39)          
Mixed class                                                                                                    2.19***                    2.98***                    2.17***          
                                                                                                                (0.15)                     (0.18)                     (0.18)          
Working class                                                                                                  2.47***                    3.47***                    2.22***          
                                                                                                                (0.13)                     (0.18)                     (0.18)          
Industrial                                                                                                                                                           2.13***          
                                                                                                                                                                      (0.25)          
Part-industrial                                                                                                                                                      3.09***          
                                                                                                                                                                      (0.19)          
Election 1900: Liberal                                  -3.30***                    -3.81***                   -5.23***                   -8.88***                   -8.23***         
                                                         (0.30)                      (0.30)                     (0.30)                     (0.35)                     (0.33)          
Election 1900: Conservative                             -2.08***                    -2.76***                   -3.94***                   -6.71***                   -5.96***         
                                                         (0.25)                      (0.25)                     (0.25)                     (0.30)                     (0.31)          
Election 1900: Neither                                  -1.76***                    -2.28***                   -3.69***                   -6.29***                   -5.78***         
                                                         (0.41)                      (0.33)                     (0.34)                     (0.33)                     (0.33)          
Cons. vote 1900                                         -0.10***                    -0.12***                   -0.19***                   -0.34***                   -0.34***         
                                                         (0.03)                      (0.03)                     (0.03)                     (0.03)                     (0.03)          
Cons. vote 1900 squared                                   0.001                      0.001*                    0.002***                   0.003***                   0.004***         
                                                         (0.001)                    (0.001)                    (0.0005)                   (0.001)                    (0.001)          
Scotland                                                                            -1.36***                   -1.36***                   -1.61***                   -1.13***         
                                                                                     (0.24)                     (0.21)                     (0.49)                     (0.41)          
N                                                          556                        556                        556                        556                        556            
--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
***p < .01; **p < .05; *p < .1                                                                                                                                                        
Variables beginning `Election' indicate constituencies contested by only one, or neither, of the major parties -- the omitted category contains Lib-Con contested constituencies.     

Determinants of MPs' votes on the the Third Reading of the 1909 Finance Bill.
--------------------------------------------------------------------------------------------------------------------
                                                                  `Aye' vote                                        
                              Model 1      Model 2      Model 3      Model 4      Model 5      Model 6     Model 7  
--------------------------------------------------------------------------------------------------------------------
Trade: free trade             2.29***      2.45***       1.63**      3.64***      2.86***       1.86*     18.49***  
                              (0.66)        (0.77)       (0.67)       (0.87)       (0.95)      (0.97)      (1.33)   
Trade: neutral                1.45**       2.02***      1.61***      1.53***        1.86       2.32***      1.46*   
                              (0.62)        (0.73)       (0.55)       (0.48)       (1.16)      (0.67)      (0.78)   
Mixed socio-economic                       1.30***      1.56***        0.30         0.36        0.74        0.42    
                                            (0.45)       (0.49)       (1.22)       (1.36)      (1.12)      (1.54)   
Working class                              1.73***      1.85***        0.49         0.44        0.89        0.02    
                                            (0.49)       (0.49)       (0.97)       (1.11)      (1.10)      (1.28)   
Part-industrial                            0.87***      1.12***        0.37         0.78       1.01**       0.84    
                                            (0.26)       (0.26)       (0.84)       (0.97)      (0.45)      (0.83)   
Industrial                                 1.39***      2.07***        1.30        1.73*        0.98        1.32    
                                            (0.33)       (0.47)       (1.15)       (1.03)      (0.73)      (1.25)   
Military                                    -0.64       -1.08**       -1.38        -1.67*       -0.64      -1.69*   
                                            (0.42)       (0.43)       (0.94)       (0.98)      (0.53)      (0.97)   
County (more rural)                         -0.03        -0.04        -1.26        -0.96        0.66        -0.37   
                                            (0.32)       (0.31)       (0.95)       (1.30)      (0.66)      (1.31)   
Includes region                 No            No          Yes           No          Yes          No          Yes    
Includes party                  No            No           No          Yes          Yes          No          Yes    
N                               500          500          500          500          500          169         169    
Log Likelihood                -266.68      -234.90      -217.33       -41.33       -38.20      -82.97      -30.30   
AIC                           539.36        487.81       470.66       108.66       120.40      183.93       88.60   
--------------------------------------------------------------------------------------------------------------------
***p < .01; **p < .05; *p < .1                                                                                      
Standard errors clustered by constituency type.                                                                     
Omitted categories are: protectionist trade, upper class socioceonomic, non-industrial.                             
`Split delegation' models include only MPs from parties and regions which included both `aye' and `no' votes.       

> library(Hmisc)

> library(xtable)

> library(lme4)

> library(dynsim)

> library(mvtnorm)

> #set.seed(12345)
> 
> ## 2.2 Descriptive Statistics
> macro$yearf = as.factor(macro$year)

> macro$countryf = as.factor(macro$country_name)

> # Table A1
> foe = macro[ , c("market" , "direct","top_incrate_n", "topitaxrate2", "l1.tradeallyadv" ,"l1rural.ineq", "l1.e2","votetax2","l1.rgdppc" .... [TRUNCATED] 

> stargazer(foe, digits = 2, type = "text")

=========================================================================
Statistic        N    Mean   St. Dev.   Min    Pctl(25) Pctl(75)   Max   
-------------------------------------------------------------------------
market          426   0.41     0.17     0.00     0.29     0.55     0.70  
direct          426   0.22     0.14     0.00     0.11     0.32     0.52  
top_incrate_n   402   2.21     3.37     0.00     0.00     3.20    17.10  
topitaxrate2    402   1.82     2.64     0.00     0.00     1.50    15.00  
l1.tradeallyadv 446   0.31     0.34    -0.04     0.06     0.55     1.47  
l1rural.ineq    432   0.51     0.11     0.18     0.45     0.59     0.67  
l1.e2           441   0.56     0.31     0.08     0.25     0.87     0.94  
votetax2        450   0.44     0.50      0        0        1        1    
l1.rgdppc       398 2,944.76 1,097.96 1,192.93 2,104.48 3,477.83 7,211.93
l1.trade        391  0.002    0.001    0.0004   0.001    0.002     0.01  
-------------------------------------------------------------------------

> ### Run models to generate coverage counts excluding any missing variables
> market.empty.d = lm(market ~ l1.tradeallyadv + l1rural.ineq + l1.lta.in .... [TRUNCATED] 

> ii2.marketx = lm(market ~ l1.tradeallyadv + l1rural.ineq + l1.lta.ineq 
+                  + l1.unisuffrage + votetax2 + l1.rgdppc + l1.trade 
+     .... [TRUNCATED] 

> c.obs = rep(NA, length(unique(market.empty.d$model$countryf)))

> minyear = rep(NA, length(c.obs))

> maxyear = rep(NA, length(c.obs))

> for(i in 1:length(unique(market.empty.d$model$countryf))){
+   c.obs[i] = length(which(market.empty.d$model$countryf == unique(market.empty.d$model$ .... [TRUNCATED] 

> c.ob.frame = as.data.frame(unique(market.empty.d$model$countryf))

> names(c.ob.frame) = "Country"

> c.ob.frame$Country = as.character(levels(c.ob.frame$Country))[c.ob.frame$Country]

> c.ob.frame$Country = capitalize(c.ob.frame$Country)

> c.ob.frame$Country[which(c.ob.frame$Country == "Uk")] = "United Kingdom"

> c.ob.frame$Observations = c.obs

> c.ob.frame$First = minyear

> c.ob.frame$Last = maxyear

> c.ob.frame
          Country Observations First Last
1         Belgium           43  1871 1913
2         Denmark           42  1872 1913
3          France           43  1871 1913
4         Germany           42  1872 1913
5           Italy           43  1871 1913
6     Netherlands           43  1871 1913
7          Norway            8  1906 1913
8          Sweden           43  1871 1913
9     Switzerland           33  1881 1913
10 United kingdom           43  1871 1913

> ## 2.3 Robustness: State Capacity
> 
> market.preferred.sc1 = lm(market ~ l1.tradeallyadv + l1rural.ineq + l1.lta.ineq
+                           + .... [TRUNCATED] 

> direct.preferred.sc1 = lm(direct ~ l1.tradeallyadv + l1rural.ineq + l1.lta.ineq
+                           + l1.e2 + l1.trade + l1.rgdppc + votetax .... [TRUNCATED] 

> ytax.preferred.sc1 = lm(top_incrate_n ~ l1.tradeallyadv + l1rural.ineq + l1.lta.ineq
+                         + l1.e2 + l1.trade + l1.rgdppc + vote .... [TRUNCATED] 

> htax.preferred.sc1 = lm(topitaxrate2 ~ l1.tradeallyadv + l1rural.ineq + l1.lta.ineq
+                         + l1.e2 + l1.trade + l1.rgdppc + votet .... [TRUNCATED] 

> market.preferred.sc2a = lm(market ~ l1.tradeallyadv + l1rural.ineq + l1.lta.ineq
+                            + l1.e2 + l1.trade + l1.rgdppc + votet .... [TRUNCATED] 

> direct.preferred.sc2a = lm(direct ~ l1.tradeallyadv + l1rural.ineq + l1.lta.ineq
+                            + l1.e2 + l1.trade + l1.rgdppc + votet .... [TRUNCATED] 

> ytax.preferred.sc2a = lm(top_incrate_n ~ l1.tradeallyadv + l1rural.ineq + l1.lta.ineq
+                          + l1.e2 + l1.trade + l1.rgdppc + vo .... [TRUNCATED] 

> htax.preferred.sc2a = lm(topitaxrate2 ~ l1.tradeallyadv + l1rural.ineq + l1.lta.ineq
+                          + l1.e2 + l1.trade + l1.rgdppc + vot .... [TRUNCATED] 

> market.preferred.sc2b = lm(market ~ l1.tradeallyadv + l1rural.ineq + l1.lta.ineq
+                            + l1.e2 + l1.trade + l1.rgdppc + votet .... [TRUNCATED] 

> direct.preferred.sc2b = lm(direct ~ l1.tradeallyadv + l1rural.ineq + l1.lta.ineq
+                            + l1.e2 + l1.trade + l1.rgdppc + votet .... [TRUNCATED] 

> ytax.preferred.sc2b = lm(top_incrate_n ~ l1.tradeallyadv + l1rural.ineq + l1.lta.ineq
+                          + l1.e2 + l1.trade + l1.rgdppc + vo .... [TRUNCATED] 

> htax.preferred.sc2b = lm(topitaxrate2 ~ l1.tradeallyadv + l1rural.ineq + l1.lta.ineq
+                          + l1.e2 + l1.trade + l1.rgdppc + vot .... [TRUNCATED] 

> ### Table A2
> stargazer(market.preferred.sc1, direct.preferred.sc1, ytax.preferred.sc1, htax.preferred.sc1, omit = c("year", "country", "Constant") .... [TRUNCATED] 

The determinants of tax progressivity, 1870-1913: robustness to state capacity control (1)
-----------------------------------------------------------------------------------
                             Indirect Share Direct Share Top Income Top Inheritance
                                Model 1       Model 2     Model 3       Model 4    
-----------------------------------------------------------------------------------
Labour trade advantage (LTA)    -0.65***      0.89***      -1.35        11.24**    
                                 (0.19)        (0.13)      (5.45)       (5.18)     
Inequality                       -0.38*        -0.16     -28.43***     -19.37***   
                                 (0.20)        (0.14)      (5.77)       (5.48)     
LTA : inequality                1.41***       -1.69***      4.39       -22.20***   
                                 (0.30)        (0.21)      (8.71)       (8.27)     
GDP per capita                   0.0000      0.0000***    -0.0002      -0.002***   
                                (0.0000)      (0.0000)    (0.001)       (0.001)    
Vote-tax link                   -0.07***      -0.05***    -3.94***       -0.44     
                                 (0.02)        (0.01)      (0.50)       (0.47)     
Economic franchise              -0.04***       -0.005      -0.43         -0.51     
                                 (0.02)        (0.01)      (0.45)       (0.43)     
Trade                          -24.93***      26.72***    345.46**     -343.68**   
                                 (5.27)        (3.70)     (144.61)     (137.34)    
Military capacity                0.0004       -0.003**    -0.11**        0.04      
                                (0.002)       (0.001)      (0.05)       (0.05)     
Country fixed effects              Y             Y           Y             Y       
Year fixed effects                 Y             Y           Y             Y       
N                                 348           348         354           354      
R-squared                         0.92          0.96        0.92         0.88      
Adj. R-squared                    0.91          0.95        0.90         0.86      
-----------------------------------------------------------------------------------
***p < .01; **p < .05; *p < .1                                                     

> ### Table A3
> 
> stargazer(market.preferred.sc2a, market.preferred.sc2b, direct.preferred.sc2a, direct.preferred.sc2b,  ytax.preferred.sc2a, ytax.p .... [TRUNCATED] 

The determinants of tax progressivity, 1870-1913: robustness to state capacity control (2)
------------------------------------------------------------------------------------------------------
                         Indirect Share      Direct Share        Top Income         Top Inheritance   
                        Model 1   Model 2  Model 3  Model 4   Model 5   Model 6   Model 7    Model 8  
------------------------------------------------------------------------------------------------------
Labour trade advantage  -0.56**  -0.61***  0.53***  0.65***   -11.00     1.26      -3.75      -1.19   
                        (0.22)    (0.23)    (0.17)   (0.17)   (6.94)    (6.74)     (6.42)     (6.68)  
Inequality               -0.25     -0.33   -0.70*** -0.47**  -43.75*** -20.27**  -19.46***   -14.56*  
                        (0.24)    (0.27)    (0.18)   (0.21)   (7.64)    (7.97)     (7.07)     (7.90)  
LTA : inequality        1.24***   1.31***  -1.08*** -1.26***  23.67**    4.71       0.82      -3.13   
                        (0.36)    (0.37)    (0.28)   (0.28)   (11.38)   (11.00)   (10.53)    (10.89)  
GDP per capita          -0.0000   -0.0000   0.0000  0.0000*  -0.001*** -0.001*** -0.001***  -0.001*** 
                       (0.0000)  (0.0000)  (0.0000) (0.0000) (0.0004)  (0.0004)   (0.0004)   (0.0004) 
Vote-tax link          -0.06***  -0.06***  -0.05*** -0.05*** -3.80***  -3.40***    -0.53      -0.45   
                        (0.02)    (0.02)    (0.01)   (0.01)   (0.50)    (0.47)     (0.47)     (0.47)  
Economic franchise       -0.02     -0.01    -0.01   -0.04**    -0.80   -3.52***   -1.72***   -2.29*** 
                        (0.02)    (0.02)    (0.01)   (0.02)   (0.53)    (0.65)     (0.49)     (0.64)  
Trade                  -28.94*** -28.15*** 22.05*** 20.02***   93.84    -119.02  -445.60*** -489.98***
                        (5.60)    (5.71)    (4.32)   (4.36)  (178.48)  (169.60)   (165.14)   (167.98) 
Education              -0.001**  -0.001**  -0.0000  -0.0002   -0.002     -0.02    0.04***     0.04**  
                        (0.001)   (0.001)  (0.0004) (0.0004)  (0.02)    (0.02)     (0.02)     (0.02)  
Education : franchise             -0.0002           0.0004**            0.04***                0.01   
                                 (0.0002)           (0.0002)            (0.01)                (0.01)  
Country fixed effects      Y         Y        Y        Y         Y         Y         Y          Y     
Year fixed effects         Y         Y        Y        Y         Y         Y         Y          Y     
N                         336       336      336      336       341       341       341        341    
R-squared                0.97      0.97      0.97     0.97     0.92      0.93       0.89       0.89   
Adj. R-squared           0.96      0.96      0.96     0.96     0.90      0.91       0.87       0.87   
------------------------------------------------------------------------------------------------------
***p < .01; **p < .05; *p < .1                                                                        

> ### Figure A2
> ###### a figure for the inheritance tax results controlling for education and education-suffrage interaction (supplementary material .... [TRUNCATED] 

> # pull out the covariance matrix
> cov <- vcov(htax.preferred.sc2b)

> # a set of values of z to compute the (instantaneous)
> # effect of x
> z0 <- seq(min(htax.preferred.sc2b$model$l1rural.ineq), max(htax.preferred.sc .... [TRUNCATED] 

> # calculate the instantaneous effect of x as z varies
> dy.dx <- beta.hat["l1.tradeallyadv"] + beta.hat["l1.lta.ineq"]*z0

> # calculate the standard error of each estimated effect
> se.dy.dx <- sqrt(cov["l1.tradeallyadv", "l1.tradeallyadv"] + z0^2*cov["l1.lta.ineq", "l1.l ..." ... [TRUNCATED] 

> # calculate upper and lower bounds of a 95% CI
> upr <- dy.dx + 1.96*se.dy.dx

> lwr <- dy.dx - 1.96*se.dy.dx

> # plot the ME using compactr
> 
> #par(mfrow = c(1,1))
> par(mar = c(4,5,2,2)+0.4)

> plot(density(htax.preferred.sc2b$model$l1rural.ineq), type = "l",  xlim = mm(htax.preferred.sc2b$model$l1rural.ineq), yaxt = "n", xaxt = "n", ylab = .... [TRUNCATED] 

> dens = density(htax.preferred.sc2b$model$l1rural.ineq)

> mnri = min(htax.preferred.sc2b$model$l1rural.ineq, na.rm = T)

> mxri = max(htax.preferred.sc2b$model$l1rural.ineq, na.rm = T)

> x1 = min(which(dens$x > mnri))  

> x2 = max(which(dens$x < mxri))  

> with(dens, polygon(x=c(x[c(x1,x1:x2,x2)]), y= c(0, y[x1:x2], 0), col="gray85", border = "gray85"))

> par(new = T)

> plot(z0, dy.dx, lwd = 3, type = "l", xlim = mm(htax.preferred.sc2b$model$l1rural.ineq), ylim = mm(c(upr, lwr)),
+      xlab = "Inequality",
+      y .... [TRUNCATED] 

> abline(h = 0, col = "grey60", lty = 2)

> #lines(z0, dy.dx, lwd = 3)
> lines(z0, lwr, lty = 3)

> lines(z0, upr, lty = 3)

> dev.print(device = pdf, file = "figureA2.pdf", width = 5, height = 5)
RStudioGD 
        2 

> ## 2.4 Robustness: Alternative Specifications
> 
> # recenter variables for use in HLMs:
> macro$l1.rgdppcTenKs = macro$l1.rgdppc/10000

> macro$l1.edupct = macro$l1.edu/100

> macro$l1.jaipct = macro$l1.jai/100

> macro$l1.market = macro$l1.market/100

> macro$l1.direct = macro$l1.direct/100

> macro$countrych = as.character(levels(macro$countryf))[macro$countryf]

> macro$countrych[which(macro$countrych == "united kingdom")] = "unitedkingdom"

> macro$countryf = as.factor(macro$countrych)

> ### Lagged dependent variable models
> market.preferred.asldv = lm(market ~ l1.tradeallyadv + l1rural.ineq + l1.lta.ineq
+                           .... [TRUNCATED] 

> direct.preferred.asldv = lm(direct ~ l1.tradeallyadv + l1rural.ineq + l1.lta.ineq
+                             + l1.e2 + l1.trade + l1.rgdppcTenKs  .... [TRUNCATED] 

> ytax.preferred.asldv = lm(top_incrate_n ~ l1.tradeallyadv + l1rural.ineq + l1.lta.ineq
+                           + l1.e2 + l1.trade + l1.rgdppcTen .... [TRUNCATED] 

> htax.preferred.asldv = lm(topitaxrate2 ~ l1.tradeallyadv + l1rural.ineq + l1.lta.ineq
+                           + l1.e2 + l1.trade + l1.rgdppcTenK .... [TRUNCATED] 

> ### Random effects/HL models
> market.preferred.asre = lmer(market ~ l1.tradeallyadv + l1rural.ineq + l1.lta.ineq
+                              + l .... [TRUNCATED] 

> direct.preferred.asre = lmer(direct ~ l1.tradeallyadv + l1rural.ineq + l1.lta.ineq
+                              + l1.e2 + l1.trade + l1.rgdppcTenK .... [TRUNCATED] 

> ytax.preferred.asre = lmer(top_incrate_n ~ l1.tradeallyadv + l1rural.ineq + l1.lta.ineq
+                            + l1.e2 + l1.trade + l1.rgdppcT .... [TRUNCATED] 

> htax.preferred.asre = lmer(topitaxrate2 ~ l1.tradeallyadv + l1rural.ineq + l1.lta.ineq
+                            + l1.e2 + l1.trade + l1.rgdppcTe .... [TRUNCATED] 

> ### Table A4: LDV Models
> stargazer(market.preferred.asldv,  direct.preferred.asldv,   ytax.preferred.asldv, htax.preferred.asldv, omit = c("year", .... [TRUNCATED] 

The determinants of tax progressivity, 1870-1913: robustness to alternative specifications: lagged dependent variable models
---------------------------------------------------------------------------------
                           Indirect Share Direct Share Top Income Top Inheritance
                              Model 1       Model 2     Model 3       Model 4    
---------------------------------------------------------------------------------
Labour trade advantage         -0.12         0.21**      -1.01         1.76      
                               (0.12)        (0.10)      (3.44)       (2.36)     
Inequality                     -0.11         -0.17*      -6.43*        -2.19     
                               (0.12)        (0.09)      (3.53)       (2.21)     
LTA : inequality                0.32        -0.39**       2.49         -3.52     
                               (0.20)        (0.16)      (5.39)       (3.73)     
GDP per capita (10k)           -0.03          0.06       -1.62         -1.69     
                               (0.08)        (0.06)      (2.26)       (1.54)     
Vote-tax link                  -0.01        -0.02***    -1.55***       -0.36     
                               (0.01)        (0.01)      (0.36)       (0.22)     
Economic franchise            -0.02**        -0.003       0.47         -0.27     
                               (0.01)        (0.01)      (0.31)       (0.20)     
Trade                         -8.55**        7.07**      -93.19        -5.87     
                               (3.66)        (2.89)     (95.99)       (63.66)    
Domestic Indirect sharet-1    0.75***                                            
                               (0.04)                                            
Direct sharet-1                             0.71***                              
                                             (0.04)                              
Top income ratet-1                                      0.92***                  
                                                         (0.05)                  
Top inheritance ratet-1                                               0.90***    
                                                                      (0.03)     
Country fixed effects            Y             Y           Y             Y       
Year fixed effects               Y             Y           Y             Y       
N                               378           378         386           386      
R-squared                       0.98          0.98        0.96         0.97      
Adj. R-squared                  0.98          0.98        0.95         0.97      
---------------------------------------------------------------------------------
***p < .01; **p < .05; *p < .1                                                   

> ### Table A5: RE Models
> stargazer(market.preferred.asre,  direct.preferred.asre,   ytax.preferred.asre, htax.preferred.asre, omit = c("year", "cou ..." ... [TRUNCATED] 

The determinants of tax progressivity, 1870-1913: robustness to alternative specifications: random effects models
-----------------------------------------------------------------------------
                       Indirect Share Direct Share Top Income Top Inheritance
                          Model 1       Model 2     Model 3       Model 4    
-----------------------------------------------------------------------------
Labour trade advantage    -0.68***      0.78***      -2.04       16.55***    
                           (0.17)        (0.12)      (4.67)       (4.03)     
Inequality                -0.49***      -0.33***   -30.35***     -10.05***   
                           (0.16)        (0.11)      (4.42)       (3.84)     
LTA : inequality          1.46***       -1.49***      6.34       -31.69***   
                           (0.26)        (0.19)      (7.31)       (6.33)     
GDP per capita (10k)        0.03        0.24***     -8.39***     -10.49***   
                           (0.11)        (0.08)      (3.07)       (2.72)     
Vote-tax link             -0.07***      -0.05***    -3.84***       -0.58     
                           (0.02)        (0.01)      (0.47)       (0.44)     
Economic franchise        -0.04***       -0.004      -0.32         -0.56     
                           (0.02)        (0.01)      (0.43)       (0.41)     
Trade                    -27.01***      25.48***    315.55**     -225.82*    
                           (4.87)        (3.40)     (132.51)     (122.62)    
Country random effects       Y             Y           Y             Y       
Year fixed effects           Y             Y           Y             Y       
N                           380           380         386           386      
Log Likelihood             550.87        668.49     -567.85       -544.68    
AIC                       -997.74       -1232.99    1239.70       1193.37    
BIC                       -792.85       -1028.10    1445.40       1399.07    
-----------------------------------------------------------------------------
***p < .01; **p < .05; *p < .1                                               

> ### Figures from LDV models -- figure A3
> 
> beta.hat.market <- coef(market.preferred.asldv)

> # pull out the covariance matrix
> cov.market <- vcov(market.preferred.asldv)

> # a set of values of z to compute the (instantaneous)
> # effect of x
> z0 <- seq(min(market.preferred.asldv$model$l1rural.ineq), max(market.preferr .... [TRUNCATED] 

> # calculate the instantaneous effect of x as z varies
> dy.dx.market <- beta.hat.market["l1.tradeallyadv"] + beta.hat.market["l1.lta.ineq"]*z0

> # calculate the standard error of each estimated effect
> se.dy.dx.market <- sqrt(cov.market["l1.tradeallyadv", "l1.tradeallyadv"] + z0^2*cov.market .... [TRUNCATED] 

> # calculate upper and lower bounds of a 95% CI
> upr.market <- dy.dx.market + 1.96*se.dy.dx.market

> lwr.market <- dy.dx.market - 1.96*se.dy.dx.market

> # plot the ME using compactr
> 
> #par(mfrow = c(1,1))
> par(mar = c(4,5,2,2)+0.4, mfrow = c(2, 2))

> plot(density(market.preferred.asldv$model$l1rural.ineq), type = "l",  xlim = mm(market.preferred.asldv$model$l1rural.ineq), yaxt = "n", xaxt = "n",  .... [TRUNCATED] 

> dens = density(market.preferred.asldv$model$l1rural.ineq)

> mnri = min(market.preferred.asldv$model$l1rural.ineq, na.rm = T)

> mxri = max(market.preferred.asldv$model$l1rural.ineq, na.rm = T)

> x1 = min(which(dens$x > mnri))  

> x2 = max(which(dens$x < mxri))  

> with(dens, polygon(x=c(x[c(x1,x1:x2,x2)]), y= c(0, y[x1:x2], 0), col="gray85", border = "gray85"))

> par(new = T)

> plot(z0, dy.dx.market, lwd = 3, type = "l", xlim = mm(market.preferred.asldv$model$l1rural.ineq), ylim = mm(c(upr.market, lwr.market)),
+      xlab  .... [TRUNCATED] 

> abline(h = 0, col = "grey60", lty = 2)

> #lines(z0, dy.dx, lwd = 3)
> lines(z0, lwr.market, lty = 3)

> lines(z0, upr.market, lty = 3)

> ### direct
> beta.hat.direct <- coef(direct.preferred.asldv)

> # pull out the covariance matrix
> cov.direct <- vcov(direct.preferred.asldv)

> # a set of values of z to compute the (instantaneous)
> # effect of x
> z0 <- seq(min(direct.preferred.asldv$model$l1rural.ineq), max(direct.preferr .... [TRUNCATED] 

> # calculate the instantaneous effect of x as z varies
> dy.dx.direct <- beta.hat.direct["l1.tradeallyadv"] + beta.hat.direct["l1.lta.ineq"]*z0

> # calculate the standard error of each estimated effect
> se.dy.dx.direct <- sqrt(cov.direct["l1.tradeallyadv", "l1.tradeallyadv"] + z0^2*cov.direct .... [TRUNCATED] 

> # calculate upper and lower bounds of a 95% CI
> upr.direct <- dy.dx.direct + 1.96*se.dy.dx.direct

> lwr.direct <- dy.dx.direct - 1.96*se.dy.dx.direct

> plot(density(direct.preferred.asldv$model$l1rural.ineq), type = "l",  xlim = mm(direct.preferred.asldv$model$l1rural.ineq), yaxt = "n", xaxt = "n",  .... [TRUNCATED] 

> dens = density(direct.preferred.asldv$model$l1rural.ineq)

> mnri = min(direct.preferred.asldv$model$l1rural.ineq, na.rm = T)

> mxri = max(direct.preferred.asldv$model$l1rural.ineq, na.rm = T)

> x1 = min(which(dens$x > mnri))  

> x2 = max(which(dens$x < mxri))  

> with(dens, polygon(x=c(x[c(x1,x1:x2,x2)]), y= c(0, y[x1:x2], 0), col="gray85", border = "gray85"))

> par(new = T)

> plot(z0, dy.dx.direct, lwd = 3, type = "l", xlim = mm(direct.preferred.asldv$model$l1rural.ineq), ylim = mm(c(upr.direct, lwr.direct)),
+      xlab  .... [TRUNCATED] 

> abline(h = 0, col = "grey60", lty = 2)

> #lines(z0, dy.dx, lwd = 3)
> lines(z0, lwr.direct, lty = 3)

> lines(z0, upr.direct, lty = 3)

> ### top income
> beta.hat.ytax <- coef(ytax.preferred.asldv)

> # pull out the covariance matrix
> cov.ytax <- vcov(ytax.preferred.asldv)

> # a set of values of z to compute the (instantaneous)
> # effect of x
> z0 <- seq(min(ytax.preferred.asldv$model$l1rural.ineq), max(ytax.preferred.a .... [TRUNCATED] 

> # calculate the instantaneous effect of x as z varies
> dy.dx.ytax <- beta.hat.ytax["l1.tradeallyadv"] + beta.hat.ytax["l1.lta.ineq"]*z0

> # calculate the standard error of each estimated effect
> se.dy.dx.ytax <- sqrt(cov.ytax["l1.tradeallyadv", "l1.tradeallyadv"] + z0^2*cov.ytax["l1.l ..." ... [TRUNCATED] 

> # calculate upper and lower bounds of a 95% CI
> upr.ytax <- dy.dx.ytax + 1.96*se.dy.dx.ytax

> lwr.ytax <- dy.dx.ytax - 1.96*se.dy.dx.ytax

> plot(density(ytax.preferred.asldv$model$l1rural.ineq), type = "l",  xlim = mm(ytax.preferred.asldv$model$l1rural.ineq), yaxt = "n", xaxt = "n", ylab .... [TRUNCATED] 

> dens = density(ytax.preferred.asldv$model$l1rural.ineq)

> mnri = min(ytax.preferred.asldv$model$l1rural.ineq, na.rm = T)

> mxri = max(ytax.preferred.asldv$model$l1rural.ineq, na.rm = T)

> x1 = min(which(dens$x > mnri))  

> x2 = max(which(dens$x < mxri))  

> with(dens, polygon(x=c(x[c(x1,x1:x2,x2)]), y= c(0, y[x1:x2], 0), col="gray85", border = "gray85"))

> par(new = T)

> plot(z0, dy.dx.ytax, lwd = 3, type = "l", xlim = mm(ytax.preferred.asldv$model$l1rural.ineq), ylim = mm(c(upr.ytax, lwr.ytax)),
+      xlab = "Inequ ..." ... [TRUNCATED] 

> abline(h = 0, col = "grey60", lty = 2)

> #lines(z0, dy.dx, lwd = 3)
> lines(z0, lwr.ytax, lty = 3)

> lines(z0, upr.ytax, lty = 3)

> ## top inheritance
> beta.hat.htax <- coef(htax.preferred.asldv)

> # pull out the covariance matrix
> cov.htax <- vcov(htax.preferred.asldv)

> # a set of values of z to compute the (instantaneous)
> # effect of x
> z0 <- seq(min(htax.preferred.asldv$model$l1rural.ineq), max(htax.preferred.a .... [TRUNCATED] 

> # calculate the instantaneous effect of x as z varies
> dy.dx.htax <- beta.hat.htax["l1.tradeallyadv"] + beta.hat.htax["l1.lta.ineq"]*z0

> # calculate the standard error of each estimated effect
> se.dy.dx.htax <- sqrt(cov.htax["l1.tradeallyadv", "l1.tradeallyadv"] + z0^2*cov.htax["l1.l ..." ... [TRUNCATED] 

> # calculate upper and lower bounds of a 95% CI
> upr.htax <- dy.dx.htax + 1.96*se.dy.dx.htax

> lwr.htax <- dy.dx.htax - 1.96*se.dy.dx.htax

> plot(density(htax.preferred.asldv$model$l1rural.ineq), type = "l",  xlim = mm(htax.preferred.asldv$model$l1rural.ineq), yaxt = "n", xaxt = "n", ylab .... [TRUNCATED] 

> dens = density(htax.preferred.asldv$model$l1rural.ineq)

> mnri = min(htax.preferred.asldv$model$l1rural.ineq, na.rm = T)

> mxri = max(htax.preferred.asldv$model$l1rural.ineq, na.rm = T)

> x1 = min(which(dens$x > mnri))  

> x2 = max(which(dens$x < mxri))  

> with(dens, polygon(x=c(x[c(x1,x1:x2,x2)]), y= c(0, y[x1:x2], 0), col="gray85", border = "gray85"))

> par(new = T)

> plot(z0, dy.dx.htax, lwd = 3, type = "l", xlim = mm(htax.preferred.asldv$model$l1rural.ineq), ylim = mm(c(upr.htax, lwr.htax)),
+      xlab = "Inequ ..." ... [TRUNCATED] 

> abline(h = 0, col = "grey60", lty = 2)

> #lines(z0, dy.dx, lwd = 3)
> lines(z0, lwr.htax, lty = 3)

> lines(z0, upr.htax, lty = 3)

> dev.print(device = pdf, file = "figureA3.pdf", height = 7, width = 7)
RStudioGD 
        2 

> dev.off()
null device 
          1 

> ### Long-run effects in LDV models -- figure A4
> ## Figure A4
> 
> ## four scenarios: unequalLTA, equalLTA, unequalLTD, equalLTD
> ## four tax outc .... [TRUNCATED] 

> equalLTAScenMkt = data.frame(l1.tradeallyadv = quantile(macro$l1.tradeallyadv, 0.9, na.rm = T),  
+                              l1rural.ineq  = qua .... [TRUNCATED] 

> unequalLTDScenMkt = data.frame(l1.tradeallyadv = quantile(macro$l1.tradeallyadv, 0.1, na.rm = T),  
+                                l1rural.ineq  = .... [TRUNCATED] 

> equalLTDScenMkt = data.frame(l1.tradeallyadv = quantile(macro$l1.tradeallyadv, 0.1, na.rm = T),  
+                              l1rural.ineq  = qua .... [TRUNCATED] 

> scenMkt4corners = list(equalLTAScenMkt, unequalLTAScenMkt, equalLTDScenMkt, unequalLTDScenMkt)

> Sim4 <- dynsim(obj = market.preferred.asldv, ldv = 'l1.market',
+                scen = scenMkt4corners, n = 20)     

> dynsimGG(Sim4, ylab = "Domestic indirect tax share", color = 'Set1',leg.labels = c("Equal, high LTA", "Unequal, high LTA", "Equal, low LTA", "Unequa ..." ... [TRUNCATED] 

> dev.print(device = pdf, file = "figureA4a.pdf", width = 7.5, height = 4)    
RStudioGD 
        2 

> dev.off()
null device 
          1 

> ### Direct tax shares:
> unequalLTAScenDir = data.frame(l1.tradeallyadv = quantile(macro$l1.tradeallyadv, 0.9, na.rm = T),  
+                       .... [TRUNCATED] 

> equalLTAScenDir = data.frame(l1.tradeallyadv = quantile(macro$l1.tradeallyadv, 0.9, na.rm = T),  
+                              l1rural.ineq  = qua .... [TRUNCATED] 

> unequalLTDScenDir = data.frame(l1.tradeallyadv = quantile(macro$l1.tradeallyadv, 0.1, na.rm = T),  
+                                l1rural.ineq  = .... [TRUNCATED] 

> equalLTDScenDir = data.frame(l1.tradeallyadv = quantile(macro$l1.tradeallyadv, 0.1, na.rm = T),  
+                              l1rural.ineq  = qua .... [TRUNCATED] 

> scenDir4corners = list(equalLTAScenDir, unequalLTAScenDir, equalLTDScenDir, unequalLTDScenDir)

> test = list(unequalLTAScenDir)

> Sim4dir <- dynsim(obj = direct.preferred.asldv, ldv = 'l1.direct',
+                   scen = scenDir4corners, n = 20)                

> dynsimGG(Sim4dir, ylab = "Direct tax share", color = 'Set1', leg.labels = c("Equal, high LTA", "Unequal, high LTA", "Equal, low LTA", "Unequal, low  ..." ... [TRUNCATED] 

> dev.print(device = pdf, file = "figureA4b.pdf", width = 7.5, height = 4)   
RStudioGD 
        2 

> dev.off()
null device 
          1 

> unequalLTAScenYtax = data.frame(l1.tradeallyadv = quantile(macro$l1.tradeallyadv, 0.9, na.rm = T),  
+                                 l1rural.ineq  .... [TRUNCATED] 

> equalLTAScenYtax = data.frame(l1.tradeallyadv = quantile(macro$l1.tradeallyadv, 0.9, na.rm = T),  
+                               l1rural.ineq  = q .... [TRUNCATED] 

> unequalLTDScenYtax = data.frame(l1.tradeallyadv = quantile(macro$l1.tradeallyadv, 0.1, na.rm = T),  
+                                 l1rural.ineq  .... [TRUNCATED] 

> equalLTDScenYtax = data.frame(l1.tradeallyadv = quantile(macro$l1.tradeallyadv, 0.1, na.rm = T),  
+                               l1rural.ineq  = q .... [TRUNCATED] 

> scenYtax4corners = list(equalLTAScenYtax, unequalLTAScenYtax, equalLTDScenYtax, unequalLTDScenYtax)

> test = list(unequalLTAScenYtax)		

> Sim4ytax <- dynsim(obj = ytax.preferred.asldv, ldv = 'l1.topincome',
+                    scen = scenYtax4corners, n = 20)                

> dynsimGG(Sim4ytax, ylab = "Top income tax rate", color = 'Set1', leg.labels = c("Equal, high LTA", "Unequal, high LTA", "Equal, low LTA", "Unequal,  ..." ... [TRUNCATED] 

> dev.print(device = pdf, file = "figureA4c.pdf", width = 7.5, height = 4)   
RStudioGD 
        2 

> dev.off()
null device 
          1 

> ### Top Inheritance Tax Rates
> 
> unequalLTAScenHtax = data.frame(l1.tradeallyadv = quantile(macro$l1.tradeallyadv, 0.9, na.rm = T),  
+            .... [TRUNCATED] 

> equalLTAScenHtax = data.frame(l1.tradeallyadv = quantile(macro$l1.tradeallyadv, 0.9, na.rm = T),  
+                               l1rural.ineq  = q .... [TRUNCATED] 

> unequalLTDScenHtax = data.frame(l1.tradeallyadv = quantile(macro$l1.tradeallyadv, 0.1, na.rm = T),  
+                                 l1rural.ineq  .... [TRUNCATED] 

> equalLTDScenHtax = data.frame(l1.tradeallyadv = quantile(macro$l1.tradeallyadv, 0.1, na.rm = T),  
+                               l1rural.ineq  = q .... [TRUNCATED] 

> scenHtax4corners = list(equalLTAScenHtax, unequalLTAScenHtax, equalLTDScenHtax, unequalLTDScenHtax)

> Sim4htax <- dynsim(obj = htax.preferred.asldv, ldv = 'l1.topinher',
+                   scen = scenHtax4corners, n = 20)                

> dynsimGG(Sim4htax, ylab = "Top inheritance tax rate", color = 'Set1', leg.labels = c("Equal, high LTA", "Unequal, high LTA", "Equal, low LTA", "Uneq ..." ... [TRUNCATED] 

> dev.print(device = pdf, file = "figureA4d.pdf", width = 7.5, height = 4)   
RStudioGD 
        2 

> dev.off()
null device 
          1 

> ###### Table A6: Taxes as a share of GDP
> size.empty = lm(Central_t_y ~ l1.tradeallyadv + l1rural.ineq + l1.lta.ineq
+                 + as.factor( .... [TRUNCATED] 

> size.preferred = lm(Central_t_y ~ l1.tradeallyadv + l1rural.ineq + l1.lta.ineq
+                     + l1.e2 + l1.trade + l1.rgdppc + votetax2
+     .... [TRUNCATED] 

> stargazer(size.empty, size.preferred
+           , omit = c("year", "country", "Constant")
+           , type = "text", style = "ajps", omit.stat =  .... [TRUNCATED] 

The determinants of domestic indirect tax revenues as a share of total tax revenue, 1871-1913
----------------------------------------------
                                 Taxes/GDP    
                             Model 1  Model 2 
----------------------------------------------
Labour trade advantage (LTA) 6.51*** 16.38*** 
                             (2.37)   (2.79)  
Inequality                    -2.96    4.98*  
                             (1.96)   (2.76)  
LTA:Inequality               -7.24** -23.28***
                             (3.52)   (4.38)  
GDP per capita                       0.001*** 
                                     (0.0002) 
Vote-tax link                          -0.40  
                                      (0.29)  
Economic franchise                     0.90   
                                      (0.64)  
Trade                                583.25***
                                     (161.01) 
Country fixed effects           Y        Y    
Year fixed effects              Y        Y    
N                              353      310   
R-squared                     0.96     0.97   
Adj. R-squared                0.96     0.97   
----------------------------------------------
***p < .01; **p < .05; *p < .1                

> ### 3. The British Case in Comparative Context
> ## Figure A5
> 
> ineq19001910 = c(by(macro$l1rural.ineq[which(macro$year >= 1900 & macro$year <= 1 .... [TRUNCATED] 

> ineq18701889 = c(by(macro$l1rural.ineq[which(macro$year < 1900 )], macro$Country_name[which( macro$year < 1900)], mean, na.rm = T))[c(2:4, 6:13)]

> shadesOfGrey <- colorRampPalette(c("grey0", "grey100"))

> threeGreys <- shadesOfGrey(3)

> candp = cbind(ineq18701889, ineq19001910)

> par(mar = c(4, 8, 1, 1)+0.2)

> cbp = barplot(t(candp[order(candp[,2]),]), horiz = T, las = 2, beside= T, col = c(threeGreys[3], threeGreys[2]), xlab = "Average inequality", xlim = .... [TRUNCATED] 

> legend(0.5, cbp[6], fill = c(threeGreys[3], threeGreys[2]), legend = c("1870-1899", "1900-1910"), bty = "n", cex = 0.8)

> dev.print(device = pdf, file = "figureA5.pdf",  width = 5.5, height = 6.5)
RStudioGD 
        2 

> dev.off()
null device 
          1 

> ### 4. Coding the Historical Record
> ## Table A8: industrialisation by county/borough
> load("trade_redist_brit_constit.RData") ## data is called b .... [TRUNCATED] 

> tti1 = table(britcon$d.type, britcon$industrial.f)

> tti2 = prop.table(tti1, 1)

> boroughs = c(tti1[1,], sum(tti1[1,]))

> share.boroughs = c(round(tti2[1,], digits = 2), NA)

> counties = c(tti1[2,], sum(tti1[2,]))

> share.counties = c(round(tti2[2,], digits = 2), NA)

> total = apply(tti1, 2, sum)

> total = c(total, sum(total))

> a8 = as.data.frame(rbind(boroughs, share.boroughs, counties, share.counties, total))

> a8
                    0      1      2  V4
boroughs        76.00  83.00 107.00 266
share.boroughs   0.29   0.31   0.40  NA
counties       135.00  93.00  62.00 290
share.counties   0.47   0.32   0.21  NA
total          211.00 176.00 169.00 556

> ### 5. Ease of Labour entry in 1906
> ## Table A10
> 
> summary(britcon$labelease)
  0   1   2 
481  34  41 

> ## Figure A7
> ur = unique(britcon$region2)

> labunop = rep(NA, length(ur))

> for(i in 1:length(ur)){
+   labunop[i] = length(which(britcon$region2 == ur[i] & britcon$labelease == 2))
+   }

> par(mar = c(5, 8, 2, 2)+0.2)

> barplot(labunop[order(labunop)], names.arg = ur[order(labunop)], horiz = 2, las = 2, col = "darkgrey")

> dev.print(device = pdf, file= "figureA7.pdf",  width = 7.5, height = 3.5)
RStudioGD 
        2 

> dev.off()
null device 
          1 

> ## Table A11: Bivariate model -- Labour candidates unopposed
> 
> britcon$lab.unopp = as.numeric(britcon$labelease == 2)

> a11 = glm(lab.unopp ~ trade.gen.f + double + d.type + lb.socec + industrial.f + contest1900 + cvs1900.2+ cvs1900.22 + scotland,
+           data = b .... [TRUNCATED] 

> a11.t.vcov<-cluster.vcov(a11, britcon$ctype, force_posdef = TRUE)

> a11.coef = coeftest(a11, vcov = a11.t.vcov)

> stargazer(a11.coef
+           , style = "ajps"
+           , title="Bivariate model: Where labor candidates run unopposed."
+           , label = " ..." ... [TRUNCATED] 

Bivariate model: Where labor candidates run unopposed.
--------------------------------------------------------------------
                            Dependent variable: unopposed labor run.
--------------------------------------------------------------------
Free trade                                   1.56**                 
                                             (0.69)                 
Neutral trade                                 0.48                  
                                             (0.65)                 
Double member                               2.98***                 
                                             (0.70)                 
County                                       -0.93                  
                                             (0.67)                 
Mixed class                                 18.96***                
                                             (0.70)                 
Working class                               19.28***                
                                             (0.52)                 
Industrial                                  17.95***                
                                             (0.45)                 
Part-industrial                             19.06***                
                                             (0.68)                 
Election 1900: Liberal                      -13.93**                
                                             (5.53)                 
Election 1900: Conservative                 -10.83**                
                                             (5.18)                 
Election 1900: Neither                      -11.35**                
                                             (5.32)                 
Cons. vote 1900                             -0.64***                
                                             (0.23)                 
Cons. vote 1900 squared                     0.01***                 
                                            (0.002)                 
Scotland                                   -18.85***                
                                             (0.78)                 
Constant                                   -28.40***                
                                             (5.13)                 
--------------------------------------------------------------------
***p < .01; **p < .05; *p < .1                                      
Standard errors clustered by constituency type                      
Omitted categories are: protectionist, upper class socioeconomic,   
contested by Liberals and Conservatives in 1900, non-industrial     
single-member, borough.                                             

> ## 6. Votes on the People's Budget
> 
> ## Table A13: Descriptive statistics for Pelling and the People's Budget
> 
> summary(britcon$lb.socec)
  A   B   C 
 51 240 265 

> summary(britcon$trade.constit.f)
 -1   0   1 
 40 469  47 

> summary(britcon$trade.gen.f)
 -1   0   1 
 37 336 183 

> summary(britcon$industrial.f)
  0   1   2 
211 176 169 

> summary(britcon$d.type)
  b   c 
266 290 

> summary(as.factor(britcon$military))
  0   1 
519  37 

> summary(as.factor(britcon$aye))
   0    1 NA's 
 127  373   56 

> ## Figure A8: Votes on the Third Reading of the 1909 Finance Bill
> 
> ## vote numbers
> labour.n = c(length(which(britcon$aye == 1 & britcon$party2 .... [TRUNCATED] 

> liberal.n = c(length(which(britcon$aye == 1 & britcon$party2 == "Liberal")), length(which(is.na(britcon$aye) == T & britcon$party2 == "Liberal")), l .... [TRUNCATED] 

> liberal.u.n = c(length(which(britcon$aye == 1 & britcon$party2 == "Liberal Unionist")), length(which(is.na(britcon$aye) == T & britcon$party2 == "Li ..." ... [TRUNCATED] 

> unionist.n= c(length(which(britcon$aye == 1 & britcon$party == "Unionist")), length(which(is.na(britcon$aye) == T & britcon$party == "Unionist")), l .... [TRUNCATED] 

> conservative.n= c(length(which(britcon$aye == 1 & britcon$party == "Conservative")), length(which(is.na(britcon$aye) == T & britcon$party == "Conser ..." ... [TRUNCATED] 

> irish.n = c(length(which(britcon$aye == 1 & britcon$party2 == "Irish Nationalist Party")), length(which(is.na(britcon$aye) == T & britcon$party2 ==  .... [TRUNCATED] 

> other.n = c(length(which(britcon$aye == 1 & britcon$party2 == "Independent Liberal")), length(which(is.na(britcon$aye) == T & britcon$party2 == "Ind ..." ... [TRUNCATED] 

> ## vote percentages
> labour = labour.n/sum(labour.n)

> liberal = liberal.n/sum(liberal.n)

> liberal.u = liberal.u.n/sum(liberal.u.n)

> unionist = unionist.n/sum(unionist.n)

> conservative = conservative.n/sum(conservative.n)

> irish = irish.n/sum(irish.n)

> other = other.n/sum(other.n)

> vote = rbind(liberal, labour, liberal.u, conservative, unionist)

> rownames(vote) = c("Liberal", "Labour", "Liberal \n Unionist", "Conservative", "Unionist")

> vote = vote*100

> fourGreys <- shadesOfGrey(4)

> par(mar = c(4,6,4,4)+0.3)

> plot(seq(0,100, length = 100), seq(0,6,length=100), col = "transparent", xaxt = "n", yaxt = "n", xlab = "", ylab = "")

> mtext(rownames(vote), side = 2, at = c(1,2,3,4,5), las = 1, line = 1)

> segments(x0 = rep(0, 5), x1 = vote[,1], y0 =c(1,2,3,4,5), y1 = c(1,2,3,4,5), lwd = 4, lend = 1)

> segments(x0 = rep(100, 5), x1 = 100-vote[,3], y0 =c(1,2,3,4,5), y1 = c(1,2,3,4,5), lwd = 4, lend = 1, col = fourGreys[3])

> axis(1)

> mtext("Share of MPs voting 'Aye'", adj = 0, side = 1, line = 2.5)

> axis(3, at = c(0, 20, 40, 60, 80, 100), labels = c(100, 80, 60, 40, 20, 0), col.axis = fourGreys[3])

> mtext("Share of MPs voting 'No'", adj = 1, side = 3, line = 2.5, col = fourGreys[3])

> mtext(side = 4, at = c(1,2,3,4,5), c(sum(liberal.n), sum(labour.n), sum(liberal.u.n), sum(conservative.n), sum(unionist.n)), line = 1, las = 2)

> mtext(side = 4, at = 6, text = "No. of \n MPs", line = 1, las=2)

> dev.print(device = pdf, file = "figureA8.pdf", width = 5.5, height = 3.5)
RStudioGD 
        2 

> dev.off()
null device 
          1 

> ## Figure A9 -- Expected Values, Party Model
> 
> m2f.type = glm(aye ~ trade.gen.f + military + industrial.f + d.type + lb.socec + party2, family=bi .... [TRUNCATED] 

> m2f.t.vcov<-cluster.vcov(m2f.type, britcon$ctype)

> m2f.t.coef = coeftest(m2f.type, m2f.t.vcov)

> m2f.sim <- rmvnorm(1000, m2f.type$coef, m2f.t.vcov)

> # Set the values of the independent variables
> freetrade.2libx <- c(1, #intercept
+                      0, # not neutral
+                      1, .... [TRUNCATED] 

> protection.2libx <- c(1, #intercept
+                          0, # not neutral
+                          0, # not free trade --> omitted category  .... [TRUNCATED] 

> freetrade.2conx <- c(1, #intercept
+                      0, # not neutral
+                      1, # free trade
+                      0, # not mi .... [TRUNCATED] 

> protection.2conx <- c(1, #intercept
+                       0, # not neutral
+                       0, # not free trade --> omitted category is pro .... [TRUNCATED] 

> pp.2freetrade <- numeric(1000)

> for(j in 1:1000) {
+   pp.2freetrade[j] <- t(as.matrix(freetrade.2libx)) %*% m2f.sim[j,]
+ }

> pe.2ft <- mean(inv.logit(pp.2freetrade))

> lo.2ft <- quantile(inv.logit(pp.2freetrade), .025)

> hi.2ft <- quantile(inv.logit(pp.2freetrade), .975)

> pp.2protection <- numeric(1000)

> for(j in 1:1000) {
+   pp.2protection[j] <- t(as.matrix(protection.2libx)) %*% m2f.sim[j,]
+ }

> pe.2pr <- mean(inv.logit(pp.2protection))

> lo.2pr <- quantile(inv.logit(pp.2protection), .025)

> hi.2pr <- quantile(inv.logit(pp.2protection), .975)

> pp.2con.freetrade <- numeric(1000)

> for(j in 1:1000) {
+   pp.2con.freetrade[j] <- t(as.matrix(freetrade.2conx)) %*% m2f.sim[j,]
+ }

> pe.2conft <- mean(inv.logit(pp.2con.freetrade))

> lo.2conft <- quantile(inv.logit(pp.2con.freetrade), .025)

> hi.2conft <- quantile(inv.logit(pp.2con.freetrade), .975)

> pp.2con.protection <- numeric(1000)

> for(j in 1:1000) {
+   pp.2con.protection[j] <- t(as.matrix(protection.2conx)) %*% m2f.sim[j,]
+ }

> pe.2conpr <- mean(inv.logit(pp.2con.protection))

> lo.2conpr <- quantile(inv.logit(pp.2con.protection), .025)

> hi.2conpr <- quantile(inv.logit(pp.2con.protection), .975)

> par(mar = c(4,6,1,1)+0.2)

> bpep = barplot(height = c(pe.2ft, pe.2pr, pe.2conft, pe.2conpr), horiz = T, xlim = c(-0.05, 1.05), xlab = "Expected probability of `aye'", col = c(" ..." ... [TRUNCATED] 

> segments(lo.2ft, bpep[1], hi.2ft, bpep[1], lty=3)

> segments(lo.2pr, bpep[2], hi.2pr, bpep[2], lty=3)

> points(x = c(lo.2ft,  hi.2ft), y = c(bpep[1],bpep[1]), pch = 18)

> points(x = c(lo.2pr,  hi.2pr), y = c(bpep[2],bpep[2]), pch = 18)

> segments(lo.2conft, bpep[3], hi.2conft, bpep[3], lty=3)

> segments(lo.2conpr, bpep[4], hi.2conpr, bpep[4], lty=3)

> points(x = c(lo.2conft,  hi.2conft), y = c(bpep[3],bpep[3]), pch = 18)

> points(x = c(lo.2conpr,  hi.2conpr), y = c(bpep[4],bpep[4]), pch = 18)

> legend(0.4, bpep[4], fill = c("#b2df8a", "#a6cee3"), legend = c("Conservative/ Unionist", "Liberal"), bty = "n", yjust = 0.5)

> dev.print(device = pdf, file = "figureA9.pdf", width = 6.5, height = 5.5)
RStudioGD 
        2 

> dev.off()
null device 
          1 
